Network inference via clustered fused graphical lasso

Network Inference;Time Series;Graphical Lasso

Zhu, Yizhi ; Koyejo ; Oluwasanmi

关键词: Network Inference;    Time Series;    Graphical Lasso;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101223/ZHU-THESIS-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Estimating the dynamic connectivity structure among a system of entities has garnered much attention in recent years. While usual methods are designed to take advantage of temporal consistency to overcome noise, they conflict with the detectability of anomalies. We propose Clustered Fused Graphical Lasso (CFGL), a method using precomputed clustering information to improve the signal detectability as compared to typical Fused Graphical Lasso methods. We evaluate our method in both simulated and real-world datasets and conclude that, in many cases, CFGL can significantly improve the sensitivity to signals without a significant negative effect on the temporal consistency